Reverse geocoding systems return an address based on the closest georeferenced data item in a dataset to an input physical location, usually the latitude and longitude of the physical location. These datasets are generally comprised of street segment centerlines, each attributed with ranges of addresses, or single-parcel polygons that contain one street address attribute for each polygon. Individual addresses are calculated from the street segments by interpolation. Addresses are computed in parcel datasets using standard point-in-polygon processes. Street intersections are derived from street segments that abut or cross. Point or parcel datasets, hereinafter referred to as point datasets or point level datasets, are known to give more accurate results than street segment centerline datasets since they contain only the discrete addresses present on the street. However, point datasets require that a large amount of data be stored and searched to find an address for the input longitude and latitude and they do not contain intersection points.
Point datasets, consisting of parcel or improvement centroids for specific addresses are now available, but these are not comprehensive in their geographic coverage. These datasets can be combined with the street segment datasets to create a mixture of address object types. Current reverse geocoding systems find matches only to the nearest point or interpolated street segment, ignoring the sparse nature of the point data. This strict matching results in fewer high accuracy address matches being made.
Prior reverse geocoding systems include the reverse geocoding modules and custom software in Group One GeoStan systems that find the closest street segment centerline or point location to an input point and other geographic information systems including those from companies such as MapInfo Corporation and Environmental Systems Research Institute (ESRI). These systems find the closest street object to an input point in order to find approximate addresses. Interpolation techniques may also be employed to create the estimated address for an input point.
The sources of data for centerline and point level address matching have historically come from postal services and from digital map vendors, including census bureaus. The centerline datasets for address matching are largely complete due to their maturity and because they contain ranges of addresses rather than individual addresses. Newer point level datasets contain only one address per record and, as noted above, may not contain all addresses and lack intersections.